AlphaGo与柯洁对决：西医与中医之别

在老外眼中，围棋（Go）是一种神秘而又古老的智力游戏，其“中医式“理论涉及许多奇奇怪怪的概念。围棋的西医理论何在？

2016年，围棋的“西医理论”出现了，论文发表在《自然》杂志上，使用数学的蒙特卡洛方法从概率意义上研究围棋博弈过程。对此，中国人看起来也是怪怪的。

此文原文是“Mastering the game of  with deep neural networks and tree search”，其摘要如下（论文作者：D Silver ， A Huang ， CJ Maddison ， A Guez ， L Sifre ）：

The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.

              蒙特卡洛方法，另文介绍。

  说明：西医也不是万能的，柯洁战胜AlphaGo完全是有可能的。

袁萌   5月25日